Within the arena of computer vision, a number of powerful algorithms exist to solve large dataset problems that enable computer systems to analyze, interpret, and classify images. However, generating images with higher and higher resolution increases the amount of time to perform image segmentations, stereo matching, or image restorations. Previously, to reduce computational cost and increase speed, computer vision algorithms have merged image pixels into super-pixels based on color or appearance information associated with the pixels. These algorithms assume that pixels having similar color or appearance are likely to take the same label and can be merged into a super-pixel. This assumption can lead to a significant amount of mislabeling of individual pixels which result in inaccuracies in the image analysis techniques described above. Accordingly, a need remains for techniques that minimize processing time and increase the accuracy of image analysis techniques.